A Vision-Based System Design and Implementation for Accident Detection and Analysis via Traffic Surveillance Video

In this work, we aim to investigate the problem of detecting and analyzing traffic accidents automatically and effectively through surveillance videos and implement the whole framework on an AI demo board. First, the technique of motion interaction field (MIF) that has the potential to detect crashes in a video is adopted to locate the crashed vehicles based on the interactions between multiple moving objects. Second, the YOLO v3 model is employed to identify the crashed vehicles within the appropriate location. In order to recover the vehicle trajectories before the collision, a hierarchical clustering approach is used, and the corresponding trajectories are obtained. Third, to facilitate the judgment of traffic police, the trajectory is projected to a vertical view by using a perspective transformation. The vehicle velocity is estimated accordingly with the unbiased finite impulse response (UFIR) approach that does not require statistical knowledge of the external noise. Then, the estimated velocity and the obtained collision angle from the vertical view can be utilized to analyze the traffic accident. Finally, to show the effectiveness and implementation performance of the proposed approach, an experiment is carried out based on a Huawei AI demo board named HiKey970 that is used for coding all the mentioned algorithms. Several accident surveillance videos act as the input of the demo board. The accidents are detected successfully, and the corresponding vehicle trajectories are recovered.